Problem
Research questions and friction points this paper is trying to address.
online criticism
partisan news media
weakly supervised learning
information ecosystem
hyperpartisanship
Characterizing Online Criticism of Partisan News Media Using Weakly Supervised Learning
🤖 AI Summary
This study addresses the challenge of effectively identifying online criticism targeting partisan news media, a task hindered by scarce labeled data and the limitations of existing methods that rely solely on textual content or manual annotation. To overcome these constraints, the authors propose a weakly supervised learning framework that, for the first time, integrates tweet content with users’ historical news-sharing behavior to construct multi-source noisy labeling functions for automatically detecting critical tweets. This approach substantially advances the detection of media criticism by leveraging behavioral signals beyond text alone. Empirical analysis reveals a significant surge in media criticism during periods of political polarization and demonstrates that users are more likely to post critical remarks after exposure to unreliable or highly partisan news sources.
📝 Abstract
We propose novel methods to identify tweets that criticize partisan news sources. Prior work suggests that criticism, ridicule, and distrust of news media all play important roles in hyperpartisanship, misinformation, and filter bubble formation. Thus, understanding the prevalence and temporal dynamics of media-targeted criticism can provide us with updated tools to assess the health of the information ecosystem. There is a scarcity of labeled data for this task, and we develop a weakly supervised learning approach that leverages multiple noisy labeling functions based on both the content of the tweet as well as the historical news sharing behavior of the user. Using this classifier, we explore how tweets expressing criticism vary by user, news source, and time, finding substantial spikes in media criticism during politically polarizing events, such as the investigation into Russian interference in the 2016 U.S.~elections and the 2017 ``unite the right'' rally in Charlottesville. This type of media-targeting criticism is also more likely to occur after users have been exposed to unreliable and hyperpartisan media.
Innovation
Methods, ideas, or system contributions that make the work stand out.
weakly supervised learning
media criticism
partisan news
noisy labeling functions
information ecosystem
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